A kit for flow cytometric detection of platelets

Abstract

The application discloses a kit for detecting platelets by flow cytometry, which comprises platelet identification antibodies (anti-CD41, anti-CD61, anti-CD42a and anti-CD42b antibodies), non-platelet exclusion antibodies (anti-CD14 antibodies and anti-CD45 antibodies) and platelet activation state detection antibodies (anti-CD62p antibodies and PAC-1 antibodies), and further comprises antibody diluents, staining incubation liquids and platelet buffers. The application realizes the integration of accurate identification, interference exclusion and multi-channel activation detection of platelets by precise identity verification of multiple platelet markers, active interference exclusion of CD14 and CD45, and single-tube integrated detection of seven-color fluorescence. The application realizes the simultaneous realization of accurate identification and multi-parameter activation state evaluation of platelets in one tube of blood sample and one-time machine detection, and is suitable for the diagnosis and monitoring of various platelet-related diseases.

Description

Technical Field

[0001] This invention belongs to the field of biomonitoring technology, and more specifically, relates to a kit for detecting platelets by flow cytometry. Background Technology

[0002] Platelets are core effector cells in circulating blood, playing a crucial role not only in physiological hemostasis and pathological thrombosis but also extensively participating in pathophysiological processes such as inflammatory responses, immune regulation, and tumor metastasis. Accurate assessment of platelet count, phenotype, and functional status is of significant clinical value for the diagnosis, treatment monitoring, and prognostic evaluation of cardiovascular diseases, thrombotic diseases, and hemorrhagic diseases. Flow cytometry, with its unique advantages of requiring small sample volumes, rapid detection, high sensitivity, and the ability to perform quantitative analysis of multiple parameters, has become the gold standard technique for platelet phenotype and function analysis.

[0003] However, the current flow cytometry platelet detection methods used in clinical and research settings have the following core shortcomings: First, the accuracy of identification is insufficient. Existing methods usually rely solely on the universal markers CD41 and CD61 to delineate platelet populations. However, in the presence of giant platelets, platelet microparticles, or cell debris, they cannot accurately distinguish between intact platelets and non-platelet particles, resulting in impure target populations for subsequent analysis and distorted basic data.

[0004] Secondly, the interference exclusion mechanism is lacking. Monocytes and their fragments in the blood can express low levels of CD41 / CD61 on their surface. The current protocol does not include any non-platelet interference exclusion mechanism, which leads to these false positive events being mistakenly counted in the platelet population. This results in a systematic overestimation of activation indicators, which seriously affects the reliability of the results.

[0005] Third, the detection efficiency is low and the data is fragmented. To obtain comprehensive information, existing methods require testing the same sample in multiple tubes to obtain data on identification and different activation pathways. This not only requires a large sample size, making it unsuitable for sample-limited scenarios such as pediatrics and thrombocytopenia, but more critically, different indicators come from different test tubes and at different time points. Minor differences in cell processing and fluctuations in instrument status can introduce systematic errors, resulting in inaccurate data correlation and difficulty in truly reflecting the synergistic activation status of the same group of platelets in different signaling pathways.

[0006] In summary, existing flow cytometry platelet detection methods all suffer from core problems such as low accuracy, poor specificity, and low detection efficiency, failing to efficiently, accurately, and comprehensively obtain platelet identification, activation status, and purity information in a single test. Therefore, developing an integrated flow cytometry detection method that can achieve accurate platelet identification, effectively eliminate non-specific interference, and simultaneously detect multiple activation parameters has become an urgent need in the field of clinical platelet detection. Summary of the Invention

[0007] To address the problems existing in the prior art, the present invention aims to provide a platelet detection kit based on flow cytometry, along with corresponding detection methods and applications. This invention achieves precise identification of platelets, interference elimination, and multi-pathway activation detection through precise identification of multiple platelet biomarkers, active interference elimination via synergistic CD14 and CD45, and integrated detection in a single tube using seven-color fluorescence, thus overcoming the core deficiencies of the prior art.

[0008] To achieve the above objectives, the present invention employs the following technical solution: On one hand, the present invention provides a reagent kit for detecting platelets, comprising platelet identification antibodies, non-platelet exclusion antibodies and platelet activation status detection antibodies, wherein the non-platelet exclusion antibodies include at least one of anti-CD14 antibody and anti-CD45 antibody.

[0009] This invention addresses the shortcomings of existing technologies in terms of accuracy and specificity by constructing a multi-layered identity verification and interference exclusion system. Firstly, at the interference exclusion level, this invention innovatively incorporates CD14 and CD45 into the detection system, constructing a dual-antibody exclusion gate. CD14 is a lineage-specific marker (LPS receptor) for monocytes, expressed on the surface of classical, intermediate, and non-classical monocytes, but not on the surface of platelets. CD45 is a leukocyte common antigen (LCA), recognizing all leukocytes (lymphocytes, monocytes, granulocytes) and their fragments. This invention introduces a dual exclusion strategy in the data analysis process: further eliminating CD14-positive and / or CD45-positive events within the CD41 / CD61 double-positive population. The biological basis for this design is that although monocytes and their fragments, lymphocytes, and granulocytes and their fragments may carry platelet-derived membrane proteins or be adhered to by platelets, they essentially retain their respective lineage characteristics—expression of CD14 or CD45. By actively identifying and excluding these lineage-positive events, this invention completely removes false-positive signals from various leukocyte sources from the platelet analysis population, solving the long-standing problem of specificity in platelet flow cytometry detection.

[0010] Furthermore, the platelet identification antibodies include anti-CD41 antibody, anti-CD61 antibody, anti-CD42a antibody, and anti-CD42b antibody, and the platelet activation status detection antibodies include anti-CD62p antibody and PAC-1 antibody.

[0011] For identification, this invention introduces CD42a and CD42b as auxiliary verification markers. CD42a (GPIX) and CD42b (GPIbα) are core components of the platelet GP Ib-IX-V complex, which is stably and highly expressed on the platelet membrane (approximately 25,000 copies / platelet) and acts as a receptor for von Willebrand factor (vWF), mediating the initial adhesion of platelets to damaged endothelium. Compared to relying solely on CD41 / CD61 double positivity, this invention requires the target population to simultaneously show strong positivity for CD41 / CD61 and positivity for CD42a / CD42b. CD41 (GPIIb) and CD61 (GPIIIa) constitute the most abundant membrane protein complex on the platelet surface, GPIIb / IIIa (approximately 80,000 copies / platelet), which is a fibrinogen receptor and mediates the final common pathway for platelet aggregation. This multiple validation mechanism provides a more rigorous definition of platelets from a biological function perspective: an intact platelet necessarily possesses both the GPIIb / IIIa complex and the GP Ib-IX-V complex, while platelet fragments or microparticles often lose part of their membrane structure during formation, making it difficult to maintain the complete expression of both complexes simultaneously. This logical gating strategy based on the co-expression of functional complexes fundamentally solves the data distortion problem caused by population impurity in traditional methods.

[0012] In assessing platelet activation function, this invention selects CD62p and PAC-1 as core activation biomarkers, representing two independent pathways of platelet activation. CD62p (P-selectin) is stored on the platelet α-granule membrane and is not expressed on the surface in the resting state. Upon activation, it is exposed on the platelet surface with the release of α-granules, serving as the gold standard indicator for α-granule release and representing the platelet degranulation activation pathway. Elevated expression of CD62p is seen in pre-thrombotic states such as acute coronary syndrome and ischemic stroke. PAC-1 specifically recognizes the conformational epitope of activated GPIIb / IIIa receptors (exposing fibrinogen binding sites), directly reflecting the activation state of platelet aggregation function and serving as a core indicator for monitoring the efficacy of antiplatelet drugs. These two pathways are independent in terms of activation timing and regulatory mechanisms. CD62p positivity indicates the occurrence of degranulation events, while PAC-1 positivity indicates receptor function activation. Together, they constitute a complete description of platelet functional status. The combined detection of CD62p and PAC-1 enables fine stratification of the activation phase: double positivity indicates complete activation (highest risk of thrombosis), single positivity of CD62p indicates partial activation (degranulation but incomplete activation of aggregation function), single positivity of PAC-1 may indicate pre-activation of aggregation function or in vitro manipulation artifacts, and double positivity indicates a resting state or effective drug inhibition. This invention incorporates these two biomarkers into a single-tube detection system, ensuring that information from both pathways is obtained within the same population and at the same time point, making the data inherently comparable and valuable for correlation analysis.

[0013] Furthermore, the antibody is labeled with a detectable fluorescent label, which is selected from at least one of FITC, PE, PE-Cy7, PerCP-Cy5.5, Alexa Fluor647, APC-Cy7, and APC.

[0014] In some embodiments, the anti-CD42a antibody is labeled FITC, the anti-CD62p antibody is labeled PE, the anti-CD41 antibody and the anti-CD61 antibody are labeled PE-Cy7, the anti-CD14 antibody is labeled PerCP-Cy5.5, the anti-CD45 antibody is labeled APC, the PAC-1 antibody is labeled Alexa Fluor 647, and the anti-CD42b antibody is labeled APC-Cy7.

[0015] This invention further addresses the spectral interference problem in multicolor detection through optimized combinations of fluorescent dyes. Multicolor detection in flow cytometry relies on the effective separation of emission spectra from different fluorescent dyes. This invention employs a systematic dye allocation based on the expression abundance and biological characteristics of each biomarker: the weakly expressed activation biomarkers CD62p and PAC-1 are assigned to the brightest PE channel and the high-performance Alexa Fluor 647 channel, respectively, ensuring the detection sensitivity for weak positive signals; the highly expressed universal biomarkers CD41 and CD61 are jointly assigned to the PE-Cy7 channel, ensuring clear differentiation of strong positive groups while avoiding the occupation of scarce high-performance channels; the stably expressed platelet identity biomarkers CD42a and CD42b are assigned to the FITC and APC-Cy7 channels, respectively; and the exclusion gate antibodies CD14 and CD45 are assigned to the PerCP-Cy5.5 and APC channels, respectively. This allocation scheme follows the classic color matching principle of "using strong dyes for weak expression and weak dyes for strong expression." At the same time, by avoiding dyes with severe spectral conflict and high non-specific binding, such as PE-Cy5, and by rationally arranging the broad-spectrum marker CD45 to an independent APC channel, it achieves stable operation of the seven-color detection on a conventional flow cytometer, ensuring clear signals and minimal crosstalk in each channel.

[0016] In summary, the technical solution of this invention addresses the issue of population impurity through multi-level identity verification, resolves specific interference through CD14 and CD45 exclusion gates, overcomes incomplete functional assessment through a combination of dual-pathway activation markers, and addresses the feasibility of multicolor detection through fluorescent dye optimization. The synergistic effect of these four dimensions enables this invention to simultaneously obtain accurate platelet identity, purity, and multi-dimensional functional status in a single blood sample and a single test. Compared to existing technologies, the core advantages of this invention lie in its ability to improve the purity of the analyzed population to over 99% through multiple verifications and active exclusion, ensuring the reliability of activation data; single-tube integration reduces detection time, sample volume, and reagent costs by over 60%; and all parameters originate from the same population at the same time, allowing for subpopulation analysis and co-expression studies, providing a novel technical tool for the precise diagnosis and research of platelet-related diseases.

[0017] Furthermore, the kit also includes an antibody diluent comprising at least one of PBS, BSA, alanine, PEG, and a preservative. Experiments have demonstrated that the optimized antibody diluent, by maintaining antibody stability and reducing non-specific binding, enables CD14 and CD45 exclusion gate antibodies to more accurately recognize targets, achieving lower activation background and higher target population recovery rates. A significant synergistic effect exists between the antibody diluent and the exclusion gate.

[0018] In some methods, the preservative is sodium azide, and the concentration of sodium azide is 0.01% to 0.5%, preferably 0.1%.

[0019] Furthermore, the kit also includes a staining incubation solution comprising at least one of Tris-HCl buffer, NaCl, BSA, Tween-20, and CaCl2.

[0020] Furthermore, the kit also includes platelet buffer, which includes at least one of PBS, NaCl, KCl, MgCl2, HEPES, NaHCO3, Na2HPO4, BSA, and glucose.

[0021] Furthermore, the antibody diluent, staining incubation solution, and platelet buffer all contain preservatives, which are selected from at least one of sodium azide, ProClin 300, gentamicin, and thimerosal.

[0022] Preferably, the preservative is 0.1% sodium azide.

[0023] On the other hand, the present invention provides a method for detecting platelets based on flow cytometry, using the kit described above for detection.

[0024] Furthermore, it includes the following steps: (1) Incubate and stain the whole blood sample to be tested with the antibody mixture in the above kit; (2) The stained samples were analyzed using flow cytometry; (3) Analyze the test data.

[0025] Furthermore, the incubation conditions described in step (1) are: incubation at 25°C in the dark for 15-30 min.

[0026] In another aspect, the present invention provides the use of the kit described above in the preparation of products for the diagnosis or monitoring of platelet-related diseases.

[0027] Furthermore, the platelet-related diseases include at least one of cardiovascular diseases, cerebrovascular diseases, thrombotic diseases, hemorrhagic diseases, and platelet dysfunction diseases.

[0028] The present invention has the following beneficial effects: 1. For the first time, the CD14 and CD45 dual antibody exclusion gate was introduced into platelet flow cytometry detection, realizing the comprehensive and active exclusion of monocytes, lymphocytes, granulocytes and their fragments. This fundamentally solves the false positive problem caused by non-specific interference, making the activation data (CD62p, PAC-1) true and reliable.

[0029] 2. Using multiple markers (CD41, CD61, CD42a, CD42b) for gating allows for a more rigorous and precise definition of the "platelet" population, effectively excluding particles or other interference from platelet origin, and providing a pure target population for subsequent analysis.

[0030] 3. This invention constructs a complete reagent system that synergistically enhances the exclusion gate antibody through systematic optimization of antibody dilution solution, staining incubation solution, and platelet buffer. The optimized reagent system creates an ideal reaction microenvironment and cell state for the efficient operation of the exclusion gate, enabling it to exert its maximum purification efficiency.

[0031] 4. Through carefully optimized fluorescent dye combinations, seven key indicators are integrated into a single tube for detection. All data can be obtained with a single staining and one instrument loading, saving precious clinical samples and reagent costs. At the same time, it ensures that all parameters are obtained from the same group of platelets, at the same time point, and under the same testing conditions, resulting in excellent data correlation.

[0032] 5. The optimized combination of fluorescein and antibody and the compensation scheme ensure clear signals and minimal crosstalk in the seven-color detection, enabling this complex detection to run stably on a conventional flow cytometer and possessing the potential for clinical translation. Attached Figure Description

[0033] Figure 1 This is a scatter plot of CD41 / CD61 double-positive platelet population delineation in Example 1 of the present invention; Figure 2 This is a scatter plot of CD42a platelet authentication data from Embodiment 1 of the present invention. Figure 3 This is a scatter plot of CD42b platelet authentication data from Embodiment 1 of the present invention. Figure 4 This is a scatter plot excluding positive interference events of CD14 and CD45 in Embodiment 1 of the present invention. Figure 5 This is a scatter plot showing the expression analysis of purified platelets CD62p and PAC-1 in Example 1 of the present invention. Detailed Implementation

[0034] The present invention will be further described in detail below with reference to specific embodiments. These embodiments are only used to explain the present invention and are not intended to limit the scope of protection of the present invention. Experimental methods in the present invention that do not specify specific conditions are all conventional methods; reagents that do not specify specific sources are all commercially available conventional reagents.

[0035] Example 1: Platelet Detection Method Provided by the Invention This embodiment provides an integrated method for comprehensive platelet detection using a seven-color flow cytometry method based on the platelet detection kit of the present invention.

[0036] (1) Kit preparation a) Antibody combination: CD41-PE-Cy7, CD61-PE-Cy7, CD42b-APC-Cy7, CD14-PerCP-Cy5.5, CD62p-PE, PAC-1-Alexa Fluor 647, CD45-APC, all purchased from Biolegend, CD42a-FITC purchased from Kuangbo Biotechnology; b) Antibody dilution buffer: Prepare antibody dilution buffer according to the formula of PBS buffer (pH 7.2-7.4), 1% BSA, 0.5% alanine, 0.2% PEG, and 0.1% sodium azide. After mixing, filter through a 0.22 μm filter membrane for sterilization. Then dilute each antibody to the optimized working concentration: CD42a-FITC 2.5 μL / test, CD62p-PE 5 μL / test, CD41-PE-Cy7 2.5 μL / test, CD61-PE-Cy7 2.5 μL / test, CD14-PerCP-Cy5.5 5 μL / test, PAC-1-Alexa Fluor 647 5 μL / test, CD42b-APC-Cy7 2.5 μL / test, and CD45-APC 3 μL / test. Add antibody dilution buffer to a final volume of 35 μL / test, gently invert to mix, avoiding the generation of air bubbles, to obtain the antibody mixture. c) Staining incubation solution: Tris-HCl buffer (20mM, pH 7.4), 150 mM NaCl, 1% BSA, 0.1% Tween-20, 1mM CaCl2, 0.1% sodium azide, mix well and equilibrate to room temperature in advance; d) Platelet buffer: PBS buffer (pH 7.35-7.45), 130 mM NaCl, 3 mM KCl, 1 mM MgCl2, 10 mM HEPES, 10 mM NaHCO3, 0.5 mM Na2HPO4, 0.2% BSA, 5 mM glucose, 0.1% sodium azide; (2) Sample collection A second tube of blood was collected using sodium citrate anticoagulation of whole blood vessels (anticoagulant to whole blood volume ratio 1:9). Sample processing was completed within 2 hours after blood collection to avoid platelet activation outside the body.

[0037] (3) Sample staining and incubation a) Take 5 μL of the whole blood sample to be tested, add 35 μL of antibody mixture, and gently vortex to mix for 3 seconds to ensure that the antibody is in full contact with the sample; b) Add 100 μL of staining incubation solution, mix naturally by gravity during sample addition, and incubate at 25°C in the dark for 20 min.

[0038] c) After incubation, add 500 μL of platelet buffer solution, mix gently, and prepare the sample for instrument testing.

[0039] (4) Flow cytometry detection and data analysis: Detection was performed using a BD FACSCanto II flow cytometer, and data analysis was conducted using FlowJo software. The gating strategy is shown below: Step 1: Delineate the approximate platelet region in the FSC / SSC scatter plot; Step 2: Platelet populations were identified based on double positivity for CD41 and CD61. Results were as follows: Figure 1 As shown; Step 3: Perform auxiliary identity verification using double positive results for CD42a and CD42b, as shown below. Figure 2 , Figure 3 As shown; Step 4: Exclude CD14 and CD45 positive events (monocytes and their debris), results as follows Figure 4 As shown; Step 5: Analyze the expression of CD62p and PAC-1 in the purified platelet population. The results are as follows: Figure 5 As shown; (5) Experimental results The detection method in this embodiment can achieve precise delineation of platelet populations, with pure platelets accounting for a certain percentage of CD41 / CD61. + The proportion of the population reached over 98%; the background signals of CD62p and PAC-1 in resting platelets were extremely low, the activation signal after ADP stimulation was strong and highly specific, and there were no false positives; the repeatability of all detection parameters was good, and the coefficient of variation (CV value) was less than 3%.

[0040] Example 2 Selection of non-platelet exclusion biomarkers This embodiment verifies the necessity of selecting CD14 / CD45 dual antibodies as exclusion gate markers by comparing the detection effects of different non-platelet exclusion markers. The experiment follows the single variable principle, only replacing the exclusion gate markers. The other antibody combinations (platelet identification markers CD61-PE-Cy7, CD41-PE-Cy7, CD42a-FITC, CD42b-APC-Cy7, activation state detection markers CD62p-PE, PAC-1-Alexa Fluor 647), reagents, and detection conditions are the same as in Example 1.

[0041] This embodiment used resting platelet samples (5 healthy volunteers' sodium citrate anticoagulated whole blood) and 5 interference samples. The interference samples were artificially constructed simulated samples with a "platelet percentage of 95%". Flow cytometry verification showed that the actual percentage fluctuation range was 94.8%-95.2% for platelets and 4.8%-5.2% for monocytes / fragmentation. Exclusion gate markers were selected as CD14-PerCP-Cy5.5, CD45-APC, and CD11b-PerCP-Cy5.5. Four experimental groups were set up for each sample according to the exclusion gate marker. The control group's antibody combination did not include the exclusion gate marker. The groupings are as follows: Experimental group 1: CD14-PerCP-Cy5.5 + CD45-APC; Experimental group 2, CD14-PerCP-Cy5.5; Experimental group 3, CD45-APC; Experimental group 4: CD14-PerCP-Cy5.5 + CD11b-PerCP-Cy5.5; Control group: No exclusion gate The samples were then incubated and analyzed by flow cytometry according to the method described in Example 1. In the flow cytometry analysis, the "total number of events" refers to the sum of all particle events collected by the instrument within the detection time, including platelets, red blood cells, white blood cells, cell debris, and instrument noise. All tests were performed under the same instrument settings to ensure data comparability between different samples.

[0042] The following six indicators are statistically analyzed: ①CD41 / CD61 positivity rate, which is the percentage of CD41 and CD61 double positive events in the sample; this indicator is used to assess the overall size of the "candidate platelet" population in the sample.

[0043] ② CD42a / CD42b double positivity rate, which is the percentage of events in the corresponding exclusion gate negative population of each experimental group where both CD42a and CD42b are simultaneously positive; this indicator is used to assess the structural integrity of the final analysis population of each experimental group. CD42a / CD42b is a core component of the GP Ib-IX-V complex and is stably co-expressed on intact platelet membranes. However, platelet fragments or microparticles often lose part of the membrane structure during formation, making it difficult to maintain the complete expression of this complex simultaneously. Therefore, the higher this indicator, the higher the proportion of structurally intact platelets in the analysis population.

[0044] ③CD14 Exclusion Rate: This is the percentage of CD14-positive events in the CD41 / CD61 double-positive population. This indicator quantifies the efficiency of the CD14 exclusion gate in eliminating monocytes and their debris. In the CD14 single-gate group and the CD14+CD45 double-gate group, this indicator represents the proportion of interference excluded by the first layer. In the CD45 single-gate group and the group without an exclusion gate, this indicator is not applicable (indicated by "-").

[0045] ④ CD45 Exclusion Rate: This is the percentage of CD45-positive events in the CD41 / CD61 double-positive population. This indicator quantifies the efficiency of the CD45 exclusion gate in eliminating leukocytes and their debris. In the CD45 single-gate group, this indicator represents the proportion of interference from a single-layer exclusion. In the CD14+CD45 double-gate group, this indicator specifically refers to the proportion of CD45-positive events in the remaining CD14-negative population (i.e., the second-layer exclusion rate), rather than the CD45 positivity rate in the original population. This indicator is not applicable in the CD14 single-gate group and the group without an exclusion gate (indicated by "-").

[0046] ⑤ CD62p positivity rate, which is the percentage of CD62p-positive events in the corresponding exclusion gate negative population of each experimental group; this indicator is used to assess the level of platelet degranulation activation (α-granule release pathway). In resting platelets, this indicator should be at a very low level (usually <1%); when impurities such as monocytes or leukocyte fragments are present in the sample, if there is no effective exclusion gate, these impurities may be misjudged as CD62p positive due to nonspecific signals, leading to an increase in false positives.

[0047] ⑥ PAC-1 positivity rate, which is the percentage of PAC-1 positive events in the corresponding exclusion gate negative population of each experimental group; this indicator is used to assess the level of platelet aggregation function activation (GPIIb / IIIa receptor conformation activation). Combined detection with CD62p can comprehensively reflect the platelet activation status. Similarly, this indicator should be at an extremely low level in resting samples; contamination can lead to an increased false positive rate.

[0048] All samples were tested three times, and the average value was taken. The results are shown in Table 1.

[0049] Table 1 Comparison of detection effects of different exclusion gate markers Note: "-" indicates that there is no corresponding exclusion step or no detection indicator in this group; "*" indicates that the CD45 exclusion rate in the CD14+CD45 dual-gate group refers to the exclusion rate in the CD14-negative remaining population (second-level exclusion), rather than the CD45 positivity rate in the original population.

[0050] Without the exclusion gate, the CD41 / CD61 positivity rates for resting and interfering samples were 99.2% and 96.8%, respectively, the highest among all groups. This is because this strategy included monocyte fragments with low CD41 / CD61 expression in the positive events, leading to an overestimation of the candidate population size. The CD42a / CD42b double positivity rate was the lowest in the group without the exclusion gate (96.5% at rest, 95.8% in interfering samples), further confirming the presence of non-platelet fragments that do not express CD42a / b in the population. Activation indicators showed abnormally high values ​​in the group without the exclusion gate (8.9% / 7.5% at rest, 23.5% / 21.2% in interfering samples), completely failing to reflect the true functional state of platelets, highlighting the indispensability of the exclusion gate in platelet detection. The data from the group without the exclusion gate constituted the baseline for comparing all exclusion gate strategies. Its extremely high false positive rate of activation quantified the degree of contamination of the test results by impurities in the interfering samples, providing a benchmark for evaluating the purification efficiency of each exclusion gate.

[0051] The CD14 single-gate genotype achieved a 4.2% CD14 exclusion rate in interfering samples, reducing the positivity rates of CD62p and PAC-1 from 23.5% / 21.2% to 1.5% / 1.3%. However, 1.5% / 1.3% of the remaining CD14- population still showed activation background, higher than the 0.9% / 0.8% in the dual-gate group, indicating that leukocyte fragments (mainly of lymphocyte and granulocyte origin) of CD14- were not cleared, demonstrating the limited coverage of the single CD14 exclusion genotype.

[0052] The CD45 gate alone achieved a 10.5% exclusion rate for CD45 in interfering samples, but it faces two contradictory issues: On the one hand, the CD41 / CD61 positivity rate dropped to 88.6%, the lowest among all strategies, indicating that the pan-leukocyte expression characteristics of CD45 inevitably lead to the elimination of platelet-leukocyte aggregates formed by leukocyte adhesion, resulting in excessive loss of real platelets; on the other hand, there is still 1.8% / 1.6% activation background in the CD45-remaining population, higher than that of the CD14 gate alone. The key to this counterintuitive phenomenon is that some monocyte fragments exhibit downregulated CD45 expression or membrane loss in in vitro lysis or adhesion states, becoming a blind spot for the CD45 gate, while CD14 can stably recognize these events.

[0053] In the interfering samples, the CD14+CD45 dual-gate exclusion rate was 4.5% (core interference source), while the CD45 exclusion rate was 1.8% (CD45+ events in the remaining CD14- population, residual lymphocyte / granulocyte debris). The two exclusion layers covered completely different types of interference events with no overlap or redundancy. Ultimately, the CD14- and CD45- population accounted for 93.2% of the original CD41 / CD61 double-positive population (i.e., total purification rate), while the CD42a / b double-positive rate reached 97.8%, demonstrating that the exclusion primarily consisted of debris rather than actual platelets. The activation background decreased to 0.9% / 0.8%, the lowest among all strategies.

[0054] The exclusion criteria of CD14 and CD45 have complementary blind spots: CD14 captures monocyte debris with low CD45 expression, while CD45 captures lymphocyte / granulocyte debris that is CD14 negative. When combined, the exclusion threshold of the dual-gate system does not need to be relaxed as with the single-gate system of CD45 (to avoid inadvertently damaging platelet-leukocyte aggregates). Therefore, a balance is achieved between higher purity (low activation background) and higher recovery rate (high CD41 / CD61 positivity rate), which is unattainable with any single exclusion gate.

[0055] The CD14+CD11b combination further validated the above logic: although CD11b can recognize some monocytes, it cannot cover non-classical monocytes (low expression of CD11b) and all lymphocytes. Its interference sample activation background (2.3% / 2.1%) is significantly higher than that of the two-gate combination, proving that CD45 has a better broad-spectrum coverage than CD11b, and the complementary coverage of CD14+CD45 is better than any other combination.

[0056] In summary, this invention achieves precise purification of the platelet population through a CD14+CD45 dual-antibody exclusion gate combined with CD42a / CD42b structural integrity verification. While preserving the target population (CD41 / CD61 positivity rate >93%, CD42a / b dual positivity rate >97%), it controls the false positive rate of activation markers to below 1% (CD62p ≤0.9%, PAC-1 ≤0.8%), significantly outperforming single exclusion gate or no exclusion gate strategies. The synergistic effect of CD14 and CD45 stems from their complementary coverage of different types of interfering events, rather than a simple additive effect.

[0057] Example 3 Synergistic effect of non-platelet elimination markers This embodiment further verifies the robustness of the CD14 and CD45 dual antibody exclusion gate under different interference loads by using a series of diluted interference samples, and elucidates the synergistic mechanism of the two.

[0058] Six artificially simulated samples were prepared, with platelet percentages of 95%, 90%, 85%, 80%, 75%, and 70%, respectively. The remainder consisted of monocytes, lymphocytes, granulocytes, and their fragments (proportions were configured according to the peripheral blood routine test). Four exclusion strategies were implemented for each sample group: CD14+CD45 double exclusion, CD14 single exclusion, CD45 single exclusion, and no exclusion. Other antibody combinations (platelet identification markers CD61-PE-Cy7, CD41-PE-Cy7, CD42a-FITC, CD42b-APC-Cy7; activation status detection markers CD62p-PE, PAC-1-Alexa Fluor 647), reagents, and detection conditions were the same as in Example 1. The following indicators were statistically analyzed: ① CD41 / CD61 positivity rate; ② CD42a / CD42b double positivity rate; ③ CD62p positivity rate; ④ PAC-1 positivity rate. All samples were tested three times, and the average value was taken. The test results are shown in Table 2.

[0059] Table 2 Comparison of detection performance of different exclusion gate strategies under different interference loads As the interference load increased (platelet percentage decreased from 95% to 70%), the positive rates of activation markers for all exclusion strategies showed an upward trend, but the increase was significantly lower for the double exclusion strategy than for the single exclusion strategy. Meanwhile, the CD42a / CD42b double positivity rate remained above 96% in all exclusion strategies (97.0%-97.8% in the double-gate group) and did not decrease significantly with increasing interference load, demonstrating that the exclusion gates cleared fragments with incomplete membrane structures rather than actual platelets. The CD42a / b double positivity rate was significantly lower in the group without exclusion gates (94.2%-95.8%), further confirming the presence of non-platelet fragments that do not express CD42a / b in this population. In a high-interference environment where platelets account for 70%, the positive rates of CD62p and PAC-1 with double exclusion gate remained at low levels of 1.3% and 1.2%, while the positive rates of CD14 with single exclusion gate rose to 4.2% and 3.9%, CD45 with single exclusion gate rose to 4.8% and 4.5%, and those without exclusion gate reached as high as 51.8% and 49.5%.

[0060] Notably, the CD42a / CD42b double positivity rate remained above 96% across all exclusion gate strategies, with only a minimal decrease as interference load increased (the double-gate group decreased from 97.8% to 97.0%, a decrease of only 0.8%). This result confirms that the exclusion gate primarily clears platelet fragments or leukocyte fragments with incomplete membrane structures, rather than structurally intact true platelets. In contrast, the CD42a / CD42b double positivity rate was significantly lower in the non-exclusion gate group (94.2%-95.8%), and the decrease was more pronounced with increasing interference load (a decrease of 1.6%), further demonstrating the presence of a large number of non-platelet fragments that do not express CD42a / b in this population. The difference in CD42a / CD42b double positivity rate among different exclusion gate strategies independently validates the precise purification effect of the CD14+CD45 double exclusion gate from the perspective of structural integrity.

[0061] This result indicates a complementary synergistic effect between the CD14 and CD45 dual-antibody exclusion gates: CD14, as a monocyte lineage-specific marker, precisely identifies and eliminates the most prevalent core interference source (monocytes and their debris); CD45, as a common leukocyte antigen, broadly covers CD14-negative lymphocytes, granulocytes, and their debris. When combined, they form a dual-gate logic in flow cytometry: CD14-positive events are preferentially identified and excluded, while residual CD14-negative but CD45-positive events (such as lymphocyte debris) are captured by a second-layer exclusion gate. This tiered exclusion mechanism allows the dual exclusion gates to maintain extremely low activation background even under high interference loads, exhibiting stronger anti-interference robustness than a single exclusion gate.

[0062] Meanwhile, the dual-gate system effectively removes interference while retaining over 93% of CD41 / CD61-positive true platelets (when platelets account for 95%), avoiding the loss of the target population caused by excessive removal of platelet-leukocyte aggregates due to the CD45 single-gate system. This balance of "precisely removing impurities and maximizing the retention of the target" is a manifestation of the complementary and synergistic effect of CD14 and CD45 functions.

[0063] Example 4: Selection of platelet activation markers This embodiment verifies the necessity of selecting CD62p-PE+PAC-1-Alexa Fluor 647 as the activation marker combination by comparing the detection effects of different platelet activation markers / combinations. The experiment follows the single variable principle, only replacing the activation marker, while the other marker combinations (platelet identification markers CD61-PE-Cy7, CD41-PE-Cy7, CD42a-FITC, CD42b-APC-Cy7, and exclusion gate marker CD14-PerCP-Cy5.5), reagents, and detection conditions are the same as in Example 1.

[0064] This embodiment uses the same resting platelet samples and interference samples as in Example 2, and sets up 5 experimental groups according to the combination of activation markers, as follows: Combination 1: CD62p-PE + PAC-1-Alexa Fluor 647 (same as Example 1); Combination 2, CD62p-PE; Combination 3, PAC-1-Alexa Fluor 647; Combination 4: CD63-Alexa Fluor 647; Combination 5: CD62p-PE + CD63-Alexa Fluor 647; The samples were then incubated and analyzed by flow cytometry according to the method in Example 1. The positive rates of CD41 / CD61, CD42a / CD42b, CD62p, PAC-1, and CD63 were statistically analyzed. All samples were tested three times, and the average value was taken. The results are shown in Table 3.

[0065] Table 3 Comparison of detection effects of different combinations of activation markers Note: "-" indicates that the indicator was not detected.

[0066] The results showed that the double positivity rate of CD42a / CD42b in each group remained above 97%, and there was no significant difference between combination four / five (containing CD63) and combination one / two / three (excluding CD63). This indicates that the high background signal of CD63 originates from the basal expression and non-specific binding of this biomarker in resting platelets, rather than from platelet population impurity or structural damage. In resting samples, the positive rates of CD62p and PAC-1 in combinations one to three were all controlled below 1.0%, demonstrating good detection specificity. However, the positive rates of CD63 biomarker in combinations four and five were significantly higher than those of CD62p and PAC-1. This is because CD63, as a lysosome-associated membrane protein, is basally expressed in some resting platelets and is easily affected by non-specific binding, making it unsuitable for precise quantification of low-level activation.

[0067] Based on the detection results of interfering samples, the advantages of combination one are more prominent. This combination can still maintain a low activation background of 1.0% and 1.1% in the interfering environment, while the positive rates of activation indicators of single biomarker combinations (combination two and combination three) increased to 1.2% and 1.3%, respectively. This suggests that when detecting single biomarkers, residual trace interference signals are more likely to be misjudged as positive. The combined detection of CD62p and PAC-1, through dual verification, effectively offsets the influence of some non-specific signals, further improving the anti-interference ability of the detection.

[0068] Furthermore, the CD42a / CD42b double positivity rate remained above 97% in all experimental groups, and there was no significant difference between combinations four / five containing CD63 and combinations one / two / three without CD63 (98.0% vs 98.2% in resting samples, 97.4% vs 97.6% in interference samples). This result rules out the possibility that the high CD63 background is due to impurity in the platelet population, confirming that the high CD63 positivity rate is due to the basal expression of this marker in resting platelets and its susceptibility to non-specific binding, rather than the failure of exclusion gates or platelet identification antibodies.

[0069] The above results demonstrate that the combination of CD62p and PAC-1 exhibits irreplaceable complementary value. CD62p, as a marker molecule for platelet α-granule release, reflects the degranulation activation pathway; while PAC-1 specifically recognizes the activated conformation of GPIIb / IIIa receptors, directly associated with platelet aggregation function. The two represent two independent key pathways of platelet activation, and their combined detection can comprehensively capture the complete process of platelet activation from early activation to functional activation.

[0070] Example 5: Effect of preservatives on platelet count results To verify the impact of different preservatives and their concentrations on the detection performance and long-term stability of the kit, and to screen the optimal preservative regimen suitable for the kit of this invention, this embodiment compares the detection effects of sodium azide, ProClin 300, gentamicin, thimerosal, and a preservative-free control under long-term storage at 4°C and accelerated storage at 37°C to determine the optimal type and concentration of preservative in the antibody diluent. This embodiment uses the antibody diluent as a representative example to verify the impact of different preservatives on the detection performance of the kit. The antibody diluent is a key component of the kit with the highest antibody concentration and requires long-term storage; therefore, the preservative screening results for this component are representative. The experiment followed the single variable principle, only changing the type and concentration of preservatives in the antibody diluent. The other antibody combinations (platelet identification markers CD61-PE-Cy7, CD41-PE-Cy7, CD42a-FITC, CD42b-APC-Cy7, activation state detection markers CD62p-PE, PAC-1-Alexa Fluor 647, exclusion gate markers CD14-PerCP-Cy5.5, CD45-APC), reagents, and detection conditions were the same as in Example 1.

[0071] This embodiment uses the same interference sample as in Example 2 (an artificially constructed simulated sample with a platelet percentage of 95%). The basic formulation of the antibody dilution solution is PBS + 1% BSA + 0.5% alanine + 0.2% PEG. Based on this, the following experimental groups were set up: Sodium azide concentration screening groups: 0.05%, 0.1%, 0.2% (w / v); Other preservative control groups: ProClin 300 (15 ppm, 30 ppm, 60 ppm), gentamicin (50 μg / mL), thimerosal (0.01%); Negative control group: No preservatives added; Blank control group: No antibodies were added, and the test was performed using only antibody dilution containing the corresponding preservatives to exclude the direct influence of the preservatives themselves on the buffer system and nonspecific background.

[0072] After preparation, the antibody diluents were filtered through a 0.22 μm filter membrane for sterilization. The antibodies were then diluted to the working concentration of Example 1 to prepare antibody mixtures. The antibody mixtures were stored at 4°C for long-term storage (samples were taken for testing at 0, 7, 14, 30, 60, and 90 days).

[0073] All samples were incubated and analyzed by flow cytometry according to the method in Example 1. Three replicates were performed at each time point, and the average value was taken. The detection indicators included: ① CD41 / CD61 positivity rate; ② CD42a / CD42b double positivity rate; ③ CD62p positivity rate; ④ PAC-1 positivity rate; ⑤ appearance and microbial detection of samples at each time point (turbidity, precipitation, LB plate coating). No significant non-specific fluorescence signal (MFI < 50) was detected in the blank control group under any preservative conditions, indicating that the preservatives themselves did not produce background signals interfering with fluorescence. The stored detection results are shown in Table 4.

[0074] Table 4. Effects of different preservatives and concentrations on detection results (long-term storage at 4℃) At a sodium azide concentration of 0.05%, after 90 days of storage at 4°C, the CD62p positivity rate increased to 1.0%, while the CD41 / CD61 positivity rate decreased to 92.7%, indicating that this concentration basically meets the storage requirements, but is slightly inferior to the 0.1% concentration group. Sodium azide performed best at a 0.1% concentration: after 90 days of storage at 4°C, the CD41 / CD61 positivity rate decreased by only 0.3% (from 93.5% to 93.2%), the CD42a / b double positivity rate remained above 97.6%, and the CD62p and PAC-1 positivity rates increased by only 0.1% (from 0.8% to 0.9% and from 0.7% to 0.8%), respectively, with a clear appearance and no sterile growth. At a sodium azide concentration of 0.2%, the CD62p positivity rate increased to 1.1% after 90 days, slightly higher than the 0.1% group (0.9%), indicating that excessively high concentrations may have slight toxicity to antibodies or platelets. In summary, the optimal concentration of sodium azide is 0.1%.

[0075] At a concentration of 15 ppm, after 90 days of storage at 4°C, the CD62p positivity rate of ProClin 300 increased to 2.8%, while the CD41 / CD61 positivity rate decreased to 88.2%, and slight turbidity appeared after 60 days. With increasing concentration (30 ppm, 60 ppm), the performance decline was more significant: in the 60 ppm group, the CD62p positivity rate reached as high as 4.2% after 90 days, while the CD41 / CD61 positivity rate decreased to 85.1%. This indicates that ProClin 300 has poor preservative effect in this kit and may adversely affect antibody activity or platelet membrane proteins in a concentration-dependent manner.

[0076] After 90 days of storage at 4°C, the CD62p positivity rates of gentamicin and thimerosal increased to 1.3% and 1.2%, respectively, while the CD41 / CD61 positivity rates remained above 92%. These performances were superior to ProClin 300 and the preservative-free group, but inferior to the 0.1% sodium azide group (CD62p 0.9%). Furthermore, gentamicin has a narrow antibacterial spectrum (primarily against Gram-negative bacteria) and limited effectiveness against fungi and Gram-positive bacteria, posing a risk of microbial contamination during long-term storage. Thimerosal contains mercury, has high toxicity, and is subject to increasingly stringent environmental regulations.

[0077] Surprisingly, ProClin 300, widely considered a "universal" preservative that does not interfere with antibody activity, performed poorly in this kit. After 90 days of storage at 4°C, the CD62p positivity rate in the 0.1% sodium azide group (0.9%) was significantly lower than that in the ProClin 300 group (2.8%), and the CD41 / CD61 positivity rate (93.2% vs 88.2%) and CD42a / b double positivity rate (97.6% vs 94.8%) were both significantly better than those in the ProClin 300 group. This result suggests that ProClin 300 may interact nonspecifically with platelet membrane proteins, leading to increased platelet activation background and decreased antibody recognition ability. This result would not have been anticipated by those skilled in the art, as the ProClin series is generally considered to have no effect on reagent performance in routine flow cytometry antibody assays.

[0078] In summary, this invention, through systematic screening of different preservatives and their concentrations, determined the optimal addition concentration of sodium azide to be 0.1% (w / v). 0.1% sodium azide showed the best performance in maintaining the detection performance and long-term stability of the kit: after 90 days of storage at 4°C, CD62p ≤ 0.9%, PAC-1 ≤ 0.8%, CD41 / CD61 positivity rate remained above 93%, CD42a / b double positivity rate remained above 97%, and there was no sterile growth. Its effect was significantly better than ProClin 300, gentamicin, thimerosal, and the preservative-free control group. Although ProClin 300 is widely considered in the literature not to affect antibody activity, it led to increased activation background and decreased detection performance in this platelet detection system, and this adverse effect was concentration-dependent. This non-obvious result further highlights the overall innovation of the technical solution of this invention. Therefore, this invention preferably uses sodium azide as the preservative for the antibody diluent, with an addition concentration of 0.05%-0.2% (w / v), and most preferably 0.1% (w / v).

[0079] The preservative screening results were validated using the antibody diluent as a model. As components of the complete kit, the antibody diluent, staining incubation solution, and platelet buffer all contain preservatives to ensure the overall stability of the kit during storage and transportation. Based on the same preservative requirements and technical logic, sodium azide can also be used in the staining incubation solution and platelet buffer.

[0080] Example 6: The effect of antibody diluent on platelet detection results To improve the specificity and accuracy of the reagent kit provided by this invention for platelet identification, this embodiment compared the effects of different antibody diluent formulations. The experiment followed the single variable principle, only replacing the antibody diluent formulation. To avoid interference from preservatives on the screening of basic components, no preservatives were added to any antibody diluent formulation in this embodiment. The remaining reagents (antibody combination, staining incubation solution, platelet buffer) and detection conditions were the same as in Example 1.

[0081] This embodiment uses the same interference sample as the previous embodiment (an artificially constructed simulated sample with a platelet percentage of 95%), and the antibody diluent is set up with 8 formulations as shown in Table 5.

[0082] Table 5 Different antibody diluent formulations The eight different antibody diluent formulations were incubated and analyzed by flow cytometry according to the method in Example 1. The positive rates of CD41 / CD61, CD42a / CD42b double positivity, CD62p positivity, and PAC-1 were statistically analyzed. All samples were tested three times, and the average value was taken. The results are shown in Table 6.

[0083] Table 6. Effect of antibody dilution solution on detection results Table 6 shows that the antibody diluent (Formula 1) of "PBS+BSA+alanine+PEG" used in this invention exhibits the best overall detection effect, with a CD41 / CD61 positivity rate of 93.5% and CD62p and PAC-1 positivity rates both controlled at an extremely low level of 0.8%. The absence of PBS (Formula 2) leads to a decrease in the CD41 / CD61 positivity rate and an increase in the activation background, indicating an imbalance in the buffer's basic ionic environment, decreased antibody binding efficiency, and an increase in the positivity rate of activation markers. The absence of BSA (Formula 3) significantly increases the activation background because the absence of BSA increases non-specific signals, making it difficult for the exclusion gate to completely clear them. The absence of alanine (Formula 4) or PEG (Formula 5) also leads to an increase in the activation background, but to a slightly lesser extent, indicating that both mainly play an auxiliary stabilizing role.

[0084] Meanwhile, the CD42a / CD42b double positivity rate reached 97.8% in Formula 1, the highest among all groups; Formula 6 (casein) decreased to 96.8%, and Formula 8 (HPMC) decreased to 97.0%. This indicates that the components of the antibody diluent (PBS, BSA, alanine, PEG) not only affect the non-specific binding of antibodies, but also indirectly ensure the stable expression and antibody recognition of membrane proteins such as CD42a / CD42b by maintaining the integrity of the platelet membrane structure, further confirming the superiority of Formula 1.

[0085] Furthermore, even with simple substitutions of the components—replacing BSA with casein (Formula 6), alanine with glycine (Formula 7), or PEG with HPMC (Formula 8)—the detection results were still inferior to those of Formula 1. This indicates that the preferred components of this invention possess uniqueness: PBS maintains ion balance, BSA and alanine doubly block non-specific sites, and PEG inhibits antibody aggregation. Together, these four components ensure that the exclusion gate antibody can accurately identify and bind to interfering markers, efficiently removing impurities while minimizing misjudgments of real platelets, ultimately achieving a simultaneous improvement in detection specificity and accuracy.

[0086] Example 7: Effect of staining incubation solution on platelet detection results To verify the effect of staining incubation solutions on platelet identification results, this example compared the detection effects of different staining incubation solution formulations. The experiment followed the single-variable principle, only changing the composition of the staining incubation solutions; all other reagents (antibody combination, antibody diluent, platelet buffer) and detection conditions remained consistent with Example 1. To avoid interference from preservatives in the screening of basic components, no preservatives were added to any of the staining incubation solution formulations in this example.

[0087] This embodiment uses the same interference sample as the previous embodiment (an artificially constructed simulated sample with a platelet percentage of 95%), and the preparation of the staining incubation solution is as follows: Sample A: The staining incubation solution had the same composition as that in Example 1: Tris-HCl buffer (20 mM, pH 7.4), 150 mM NaCl, 1% BSA, 0.1% Tween-20, and 1 mM CaCl2. The mixture was thoroughly mixed and equilibrated to room temperature beforehand. Sample B: Tris-HCl buffer (20 mM, pH 7.4), 150 mM NaCl, 1% BSA, 1 mM CaCl2, mixed thoroughly and equilibrated to room temperature in advance; Sample C: Tris-HCl buffer (20 mM, pH 7.4), 150 mM NaCl, 1% BSA, 0.1% Tween-20, mixed thoroughly and equilibrated to room temperature in advance; Sample D: PBS buffer (20 mM, pH 7.4), 150 mM NaCl, 1% BSA, 0.1% Tween-20, mixed thoroughly and equilibrated to room temperature in advance; Then, the samples were incubated and analyzed by flow cytometry according to the method in Example 1. The positive rates of CD41 / CD61, CD42a / CD42b, CD62p, and PAC-1 were statistically analyzed. All samples were tested three times, and the average value was taken. The results are shown in Table 7.

[0088] Table 7. Effect of staining incubation solution on detection results The results in Table 7 show that sample A exhibited the best detection performance. The CD41 / CD61 positivity rate reached 94.0%, while the CD62p and PAC-1 positivity rates were as low as 0.8% and 0.9%, respectively. This excellent performance is attributed to the synergistic effect of each component and the exclusion gate antibody. The Tris-HCl buffer provides a stable buffering environment within the physiological pH range of 7.4. Compared to the PBS buffer (sample D), its stronger pH buffering capacity effectively resists pH fluctuations caused by metabolite release or antibody binding during incubation, laying the foundation for the specific binding of the CD14 / CD45 exclusion gate antibody to the marker and avoiding the decrease in antibody activity caused by pH drift.

[0089] 1% BSA forms a dual blocking effect with exclusion gate antibodies, reducing non-specific adsorption of antibodies to cell debris and the vessel wall by occupying non-specific binding sites, thus further reducing background signals. 0.1% Tween-20, as a non-ionic surfactant, plays a crucial role: in sample B lacking Tween-20, antibodies readily adsorb onto the vessel wall and aggregate locally, significantly increasing non-specific binding to interfering substances, leading to a surge in CD62p / PAC-1 positivity rates to 2.8% and 2.5%. These non-specific signals cannot be recognized and eliminated by exclusion gate antibodies, directly increasing the activation background. 1 mM CaCl2 is not only an essential ion for PAC-1 binding to activated GPIIb / IIIa receptors, but also stabilizes platelet membrane structure, preventing non-specific conformational changes in platelets due to incubation environment variations. In sample C lacking CaCl2, PAC-1 binding efficiency decreased, platelet membrane epitopes were abnormal, and the activation false positive rate increased to 1.8% and 1.6%.

[0090] The results of the CD42a / CD42b double positivity rate further corroborated this conclusion: sample A reached 97.9%, the highest among all groups; sample B, lacking Tween-20, decreased to 97.6%; sample C, lacking CaCl2, decreased to 97.3%; and sample D, using PBS, reached 97.5%. The trend of this indicator was completely consistent with the CD41 / CD61 positivity rate and activation indicators, independently verifying the superiority of staining incubation solution formulation A from the perspective of platelet structural integrity.

[0091] In summary, the staining incubation solution optimizes the antibody binding microenvironment through the synergistic effect of its components. This ensures that the exclusion gate antibody can accurately identify interference sources while inhibiting the generation of non-specific signals, forming a highly efficient synergy with the exclusion gate antibody and ensuring accurate and reliable test results.

[0092] Example 8: The effect of platelet buffer solution on test results To verify the effectiveness of the platelet buffer solution provided in Example 1 of this invention in improving the specificity and accuracy of platelet detection, this example further compared the effects of platelet buffer solutions with different compositions. The experiment followed the single-variable principle, only changing the platelet buffer solution formulation; all other reagents (antibody combination, antibody diluent, staining incubation solution) and detection conditions remained consistent with Example 1. To avoid interference from preservatives in the screening of basic components, no preservatives were added to any of the platelet buffer solution formulations in this example.

[0093] This embodiment uses the same interference sample as the previous embodiment (an artificially constructed simulated sample with a platelet percentage of 95%), and the composition of the platelet buffer is as follows: Formula 1: Same composition as platelet buffer in Example 1, PBS buffer (pH 7.35-7.45), 130 mM NaCl, 3 mM KCl, 1 mM MgCl2, 10 mM HEPES, 10 mM NaHCO3, 0.5 mM Na2HPO4, 0.2% BSA, 5 mM glucose; Formula 2: PBS buffer (pH 7.35-7.45), 130 mM NaCl, 3 mM KCl, 10 mM HEPES, 10 mM NaHCO3, 0.5 mM Na2HPO4, 0.2% BSA, 5 mM glucose; Formula 3: PBS buffer (pH 7.35-7.45), 130 mM NaCl, 3 mM KCl, 1 mM MgCl2, 10 mM NaHCO3, 0.5 mM Na2HPO4, 0.2% BSA, 5 mM glucose; Formula 4: PBS buffer (pH 7.35-7.45), 130 mM NaCl, 3 mM KCl, 1 mM MgCl2, 10 mM MEPES, 10 mM NaHCO3, 0.5 mM Na2HPO4; Then, the samples were incubated and analyzed by flow cytometry according to the method in Example 1. The positive rates of CD41 / CD61, CD42a / CD42b double positivity, CD62p positivity, and PAC-1 were statistically analyzed. All samples were tested three times, and the average value was taken. The results are shown in Table 8.

[0094] Table 8. Effect of platelet buffer solution on test results Data shows that optimizing the platelet buffer formulation is of great significance for maintaining detection accuracy. The formulation used in this invention achieves high platelet delineation and low activation background with a CD41 / CD61 positivity rate of 93.6% and activation index positivity rates of 0.9% and 0.8%, respectively. The reagents and antibodies work together to maintain the resting state of platelets and the specific binding ability of antibodies.

[0095] In contrast, Mg in Formula 2 2+ The absence of these components can cause partial platelet membrane structure disorder and abnormal expression of surface markers. Simultaneously, platelets are prone to slight aggregation, leading to a decrease in the CD41 / CD61 positivity rate to 91.5% and an increase in the activation false positive rate to 2.2% and 2.0%, respectively. HEPES and NaHCO3 synergistically form a potent buffer system, effectively resisting pH drift during incubation. In formulation three, which lacks HEPES, pH fluctuations induce slight platelet activation, increasing the activation false positive rate to 1.8% and 1.6%. Furthermore, the combination of BSA and glucose is indispensable for maintaining detection stability. The absence of both in formulation four leads to rapid platelet instability, significant adhesion loss, and easy non-specific activation, generating numerous endogenous interference signals. These signals originate from the target cells themselves and cannot be cleared by exclusion gate antibodies, ultimately resulting in severely distorted test results.

[0096] The results of the CD42a / CD42b double positivity rate also support the above conclusion: Formula 1 reached 97.8%, the highest among all groups; Formula 2 (deficient in Mg) 2+ The platelet GP Ib-IX-V complex expression integrity was reduced to 97.2% in formulation 1, 97.5% in formulation 2 (lacking HEPES), and significantly reduced to 96.5% in formulation 3 (lacking BSA and glucose). This result independently validates the role of Mg in platelet GP Ib-IX-V complex expression integrity. 2+ HEPES, BSA, and glucose play important roles in maintaining the stability of platelet membrane structure.

[0097] In summary, the platelet buffer solution of the present invention, through the synergistic effect of multiple components, not only provides a stable external environment for the exclusion gate antibody to perform its interference exclusion function, but also reduces the generation of endogenous interference signals by maintaining the resting state and structural integrity of platelets, ultimately achieving a significant improvement in detection accuracy and specificity.

[0098] Example 9 Optimization of Incubation Conditions This embodiment verifies the necessity of selecting 25℃ incubation at the dark for 20 min as the optimal incubation condition by comparing the effects of different combinations of incubation temperature and incubation time. The experiment follows the principle of single variable, only changing the incubation temperature and time parameters, while keeping the antibody combination, reagents and detection conditions consistent with those in Example 1.

[0099] This embodiment used resting platelet samples (5 healthy volunteers' sodium citrate anticoagulated whole blood) and set up different combinations of incubation temperature and time as experimental groups, specifically as follows: Condition 1: Incubate at 25°C for 20 min (same as in Example 1); Condition 2: Incubate at 25°C for 15 min; Condition 3: Incubate at 25°C for 30 min; Condition 4: Incubate at 4°C for 20 min; Condition 5: Incubate at 37°C for 20 min.

[0100] After sample addition and mixing were completed according to the method in Example 1, the samples were incubated in the dark under corresponding conditions. Subsequent instrument detection and data analysis steps were consistent with Example 1. The positive rates of CD41 / CD61, CD42a / CD42b double positivity, CD62p positivity, and PAC-1 were statistically analyzed. All samples were tested three times, and the average value was taken. The results are shown in Table 9.

[0101] Table 9 Comparison of detection effects under different incubation conditions The results showed that the lowest background activity (0.8% / 0.7%) and the highest CD41 / CD61 positivity rate (98.5%) were observed at 25℃ for 20 min, indicating sufficient antibody binding and stable platelet status. While the background was even lower at 15 min, insufficient staining might have affected weak positive detection. The background significantly increased at 30 min due to prolonged incubation inducing in vitro platelet activation. The recovery rate decreased at 4℃ due to platelet aggregation loss caused by low temperature and slowed antibody binding kinetics. Activation increased dramatically at 37℃, as physiological temperatures readily activate platelets.

[0102] The changing trend of CD42a / CD42b double positivity rate also supports 25℃ / 20min as the optimal condition: under this condition, it reaches 98.2%, 97.8% at 4℃ / 20min, and significantly decreases to 96.8% at 37℃ / 20min. The platelet aggregation loss caused by low temperature and the conformational changes of membrane proteins induced by high temperature can both be sensitively reflected by this index, further validating the necessity of optimizing incubation conditions.

[0103] It is evident that incubation conditions directly affect platelet activation and antibody binding efficiency; improper conditions can lead to platelet activation or loss, and these changes cannot be corrected by the exclusion gate because the target cells themselves have already been altered. The results confirm that incubation at 25°C in the dark for 20 minutes is the optimal incubation condition for this invention's kit.

[0104] Example 10 Optimization of Fluorescent Dye Distribution This embodiment verifies the rationality of the dye combination scheme of the present invention by comparing the spectral performance of different fluorescent dye distribution schemes. A total of 5 dye distribution schemes were set up, while the remaining antibody combinations, reagents, and detection conditions were consistent with those in Example 1: Option 1 (this invention): CD42a-FITC, CD62p-PE, CD41 / 61-PE-Cy7, CD14-PerCP-Cy5.5, CD45-APC, PAC-1-Alexa Fluor 647, CD42b-APC-Cy7; Option 2 (interchangeable CD62p / CD42a): CD42a-PE, CD62p-FITC, the rest is the same as Option 1; Option 3 (CD41 / 61 replaced with FITC): CD41 / 61-FITC, CD42a-PE-Cy7, the rest are the same as Option 1; Option 4 (PE-Cy5 replaces PE-Cy7): CD41 / 61-PE-Cy5, otherwise the same as Option 1; Option 5 (APC-Cy7 replaced with BV421): CD42b-BV421, the rest is the same as Option 1.

[0105] The spectral crosstalk matrix of each protocol was determined using a BD FACSCanto II flow cytometer, and the following parameters were statistically analyzed: ① CD62p detection sensitivity (mean fluorescence intensity MFI of the positive population); ② PAC-1 detection sensitivity (MFI); ③ CD42a signal-to-noise ratio; ④ CD42b signal-to-noise ratio; ⑤ spectral crosstalk rate (maximum non-target channel signal / target channel signal × 100%); ⑥ compensation stability (calculated compensation value CV%) after 20 consecutive detections; ⑦ overall detection success rate (proportion of samples with clear signals and accurate gating). The detection results are shown in Table 10.

[0106] Table 10 Performance Comparison of Different Dye Distribution Schemes The results showed that Scheme 1 achieved the best overall performance with a CD62p detection sensitivity of 2850 MFI, a PAC-1 detection sensitivity of 3200 MFI, and a CD42a signal-to-noise ratio of 45.2, resulting in an overall detection success rate of 98.5%. Scheme 2, which swapped the fluorescence of CD62p and CD42a, caused the CD62p sensitivity to plummet to 1680 MFI, a 41.1% decrease compared to Scheme 1, and the spectral crosstalk rate increased to 4.5%, reducing the success rate to 89.2%. This is because CD62p is a weakly expressed antigen and needs to be allocated to the high fluorescence channel (PE) to ensure detection sensitivity; switching it to the weak FITC channel severely suppressed the signal. Scheme 3, which swapped CD41 / 61 to FITC and CD42a to PE-Cy7, resulted in slightly lower sensitivities for both CD62p and PAC-1 compared to Scheme 1. Although the CD42a signal-to-noise ratio increased to 48.5, this was at the cost of sacrificing activation detection, reducing the success rate to 95.8%. This is because the FITC channel, when occupied by highly expressed CD41 / 61, requires a reduction in the PMT voltage, indirectly affecting the detection window of the PE channel. In Scheme Four, replacing CD41 / 61 with PE-Cy5 significantly increased the spectral crosstalk rate to 5.8%, worsened the compensation stability (CV) to 6.2%, and reduced the success rate to 91.5%. This is because the emission spectrum of PE-Cy5 overlaps with multiple channels such as PerCP-Cy5.5 and APC, resulting in severe crosstalk and unstable multicolor compensation. In Scheme Five, replacing CD42b with BV421 achieved data quality close to Scheme One, but BV421 requires UV laser (355 nm) excitation, and most clinical flow cytometers are not equipped with UV lasers, hindering its widespread application.

[0107] The CD42b signal-to-noise ratio (SNR) detection results also support the superiority of Scheme 1: Scheme 1 is 42.8, Scheme 2 is 43.5 (close), Scheme 3 drops significantly to 38.2, Scheme 4 is 41.5, and Scheme 5 is 44.0. The reason for the decrease in CD42b SNR in Scheme 3 is that the PMT voltage needs to be lowered after the FITC channel is occupied by the highly expressed CD41 / 61, indirectly affecting the detection sensitivity of the spectral range related to the FITC channel. This further confirms the scientific validity of the allocation principle of assigning highly expressed markers to PE-Cy7 and weakly expressed markers to PE / AF647.

[0108] The above results indicate that the preferred fluorescence distribution scheme of this invention follows the principles of "using strong dyes for weak expression, using weak dyes for strong expression, prioritizing fluorophores with low spectral crosstalk, and ensuring instrument versatility," achieving an optimal balance across five dimensions: detection sensitivity, signal-to-noise ratio, spectral crosstalk, detection stability, and instrument compatibility. Schemes two through five each have significant shortcomings in one dimension, leading to a decrease in overall detection success rate. The results confirm that the preferred fluorescence distribution scheme of this invention is a key technical feature for achieving single-tube seven-color platelet flow cytometry detection.

[0109] Example 11 Methodological Validation of the Reagent Kit of the Present Invention This embodiment systematically evaluates the analytical performance of the kit to confirm the reliability of its detection results.

[0110] 1. Precision verification Four indicators—CD41 / CD61, CD42a / CD42b, CD62p, and PAC-1—were selected to represent the detection stability of the identification and activation layers, respectively. Resting whole blood samples from 20 healthy volunteers were used for the following precision evaluations: Intra-batch repeatability: Within the same batch, each of the 20 samples was tested 3 times, resulting in a total of 60 data points. The total coefficient of variation (CV%) was calculated by combining the data. Inter-batch repeatability: Using three different batches of the kit, each of the same 20 samples was tested once, for a total of 60 data points. The total CV% was calculated by pooling the data. Daily repeatability: For 5 consecutive working days, the same 20 samples were tested once a day, for a total of 100 data points, and the total CV was calculated by combining the data.

[0111] The results are shown in Table 11.

[0112] Table 11 Precision Validation Results The results showed that the intra-assay total CV% for all indicators was less than 3%, and the inter-assay and inter-day total CV% were both less than 5%, meeting the precision requirements for flow cytometry detection. The intra-assay CV for CD42a / CD42b double positivity was 1.8%, the inter-assay CV was 2.5%, and the inter-day CV was 3.2%, all less than 5%, comparable to the precision of CD41 / CD61 positivity. This indicator, as an independent validation parameter for platelet structural integrity, further confirms the reliability of the detection results from the kit of this invention due to its excellent repeatability.

[0113] 2. Accuracy Verification Accuracy was evaluated using an incorporation recovery experiment. Platelet-depleted plasma was prepared by mixing platelet-rich plasma of known concentrations at a low concentration (100 × 10⁻⁶). 9 / L), Medium (200×10 9 / L), height (400×109 The CD41 / CD61 positivity rate was detected at three concentration levels ( / L), with three independent samples prepared for each level. The recovery rate of the measured value was calculated compared to the theoretical value, and the CD41 / CD61 positivity rate was selected to verify the reliability of its conversion to absolute platelet count. The detection results are shown in Table 12.

[0114] Table 12 Accuracy Verification Results The results showed that the recoveries at all concentration levels were between 90% and 110%, indicating that the kit has good accuracy.

[0115] 3. Linearity range verification The linear relationship between platelet concentration and detection signal was verified by detecting the CD41 / CD61 positivity rate. A high-concentration platelet sample (approximately 800 × 10⁻⁶) was taken. 9 The platelet-containing buffer ( / L) was serially diluted with platelet buffer at six different dilutions: 1:1, 1:2, 1:4, 1:8, 1:16, and 1:32. Three samples were prepared for each dilution, and the CD41 / CD61 positivity rate was detected, as shown in Table 13.

[0116] Table 13 Linearity Validation Results The results show that in the range of 50-800×10 9 Within the range of / L, the regression equation is y=0.986x+1.24, R0 2 =0.997, indicating a good linear relationship.

[0117] 4. Stability Verification Since activation indicators are most sensitive to reagent deterioration and in vitro activation of samples, CD62p and PAC-1 were chosen to reflect changes in stability.

[0118] (1) Reagent kit stability Store the kit at 4°C protected from light. Take samples at 0 days, 1 month, 3 months, and 6 months, and test the same sample. The test results are shown in Table 14.

[0119] Table 14 Results of reagent kit stability verification The results showed that the reagent kit remained stable after being stored at 4°C for 1, 3, and 6 months with an activation background increase of ≤ 0.3%.

[0120] (2) Sample stability Twenty samples were collected and tested after being placed at room temperature for different times. The results are shown in Table 15.

[0121] Table 15 Sample stability verification results The results showed that the activation background increased by ≤0.2% within 4 hours of sample storage at room temperature, and the results were stable. After 6 hours, the background increased significantly. This change is due to the spontaneous activation characteristics of platelets after ex vivo. It is recommended to complete the test within 4 hours after blood collection.

[0122] In summary, the kit of the present invention meets the analytical performance requirements in terms of precision, accuracy, linear range, and stability, and has good reliability and reproducibility.

[0123] While the present invention has been disclosed above, it is not limited thereto. Any person skilled in the art can make various modifications and alterations without departing from the spirit and scope of the invention; therefore, the scope of protection of the present invention should be determined by the scope defined in the claims.

Claims

1. A method for detecting platelets based on flow cytometry, characterized in that, The detection was performed using a platelet detection kit; the kit included platelet identification antibodies, non-platelet exclusion antibodies, and platelet activation status detection antibodies. The platelet identification antibodies are anti-CD41 antibody, anti-CD61 antibody, anti-CD42a antibody, and anti-CD42b antibody; The non-platelet exclusion antibodies are anti-CD14 antibodies and anti-CD45 antibodies; The antibodies used to detect platelet activation status are anti-CD62p antibody and PAC-1 antibody; The antibodies are labeled with detectable fluorescent markers: the anti-CD42a antibody is labeled FITC, the anti-CD62p antibody is labeled PE, the anti-CD41 and anti-CD61 antibodies are labeled PE-Cy7, the anti-CD14 antibody is labeled PerCP-Cy5.5, the anti-CD45 antibody is labeled APC, the PAC-1 antibody is labeled Alexa Fluor 647, and the anti-CD42b antibody is labeled APC-Cy7. It also includes antibody diluent, staining incubation solution and platelet buffer; The antibody diluent consists of the following components: PBS buffer at pH 7.2-7.4, 1% BSA, 0.5% alanine, 0.2% PEG, and 0.1% sodium azide; The staining incubation solution consists of the following components: 20 mM Tris-HCl buffer at pH 7.4, 150 mM NaCl, 1% BSA, 0.1% Tween-20, 1 mM CaCl2, and 0.1% sodium azide. The platelet buffer consists of the following components: PBS buffer (pH 7.35-7.45), 130 mM NaCl, 3 mM KCl, 1 mM MgCl2, 10 mM HEPES, 10 mM NaHCO3, 0.5 mM Na2HPO4, 0.2% BSA, 5 mM glucose, and 0.1% sodium azide. The method includes the following steps: (1) Mix the whole blood sample to be tested with the antibody mixture in the above kit, add staining incubation solution, and incubate at 25°C in the dark for 20 minutes; (2) Dilute with platelet buffer and detect using flow cytometry; (3) Analyze the detection data; the analysis includes the following gate strategy performed in sequence: Delineate the approximate platelet region in the FSC / SSC scatter plot; Platelet populations were identified by double positivity for CD41 and CD61; Assisted identity verification was performed using a double positive result for CD42a and CD42b. Excluding CD14 and CD45 positive events; The expression of CD62p and PAC-1 was analyzed in the purified platelet population.

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